Mesoscopic Simulation and Experimental Study of Phospholipid Monolayer at the Air-Water Interface

&nbsp; &nbsp; &nbsp; &nbsp;肺表面活性剂是一种吸附在肺泡表面的生物活性物质，主要由脂质和蛋白质组成。它由肺泡II型细胞组装并分泌到肺泡表面上，形成一层液体薄膜。肺表面活性剂膜是防御病原体或颗粒物吸入人体的第一道屏障，并能够降低肺泡表面张力，防止肺泡在呼吸过程中萎缩，以维持正常的呼吸作用。因此，研究呼吸过程中处于压缩和扩张作用下的肺表面活性剂单层膜的结构特征和力学性能是肺表面活性剂生物物理学的核心问题。实验方法无法直接观察到单层膜在压缩过程中结构转变的动态行为和分子构象的细微变化，而全原子分子动力学模拟和MARTINI粗粒化模拟又在模拟体系的空间和时间尺度上受限，模拟大尺度的复杂生物体系仍然面临诸多挑战。 &nbsp; &nbsp; &nbsp; 本文中我们针对肺表面活性剂中主要成分二棕榈酰磷脂酰胆碱（dipalmitoylphosphatidylcholine, DPPC）和棕榈酰油酰磷脂酰胆碱（palmitoyloleoylphosphatidylcholine, POPC）在水气界面形成的磷脂单层膜，使用约束液滴表面张力测量仪（constrained drop surfactometry, CDS）对其进行了实验测量并建立了介观尺度的单层膜粗粒化模型，并采用多体耗散粒子动力学（multi-body dissipative particle dynamics, MDPD）方法对单层膜进行了全面的模拟研究。对于饱和的DPPC和不饱和的POPC这两种磷脂分子形成的单层膜，我们发现环境温度的不同是其压缩等温线形态以及位置改变的主要因素，而实验所使用的面积压缩速率则对等温线没有显著的影响。对于DPPC单层膜，所处温度的升高导致其表面压力增加，等温线上的相共存区域也将会变得更窄。而对于具有简单相行为的POPC单层膜，温度的升高只降低了其表面张力。最后我们发现混合的DPPC/POPC单层膜表现出中间相的行为，并随着两种PC含量占比的改变而显示出不同的变化趋势。 &nbsp; &nbsp; &nbsp; &nbsp; 基于自上而下的粗粒化方案，我们建立了DPPC和POPC的单层膜介观模型，并使用MDPD模拟得到了与全原子模拟和CDS实验相似的表面压力-面积等温线，观察到与表面压力相关的单层膜形态，得到了处于不同相区域的单层膜。此外，面积压缩模量，脂质尾部的序参数、单层膜的厚度以及珠子的密度分布均与之前的模拟和实验结果在定量上相符，还捕获了混合DPPC/POPC单层膜的表面压力-面积等温线随着两种磷脂含量占比的敏感变化。综上，我们所建立的MDPD单层膜模型在计算效率更高的前提下，能够在更大时空尺度上准确模拟复杂单层膜的结构特征、力学特性和界面行为。</p

Interfacial behavior of phospholipid monolayers revealed by mesoscopic simulation

A mesoscopic model with molecular resolution is presented for dipalmitoyl phosphatidylcholine (DPPC) and pal-mitoyl oleoyl phosphatidylcholine (POPC) monolayer simulations at the air-water interface using many-body dissipative particle dynamics (MDPD). The parameterization scheme is rigorously based on reproducing the physical properties of water and alkane and the interfacial property of the phospholipid monolayer by comparison with experimental results. Using much less computing cost, these MDPD simulations yield a similar surface pressure-area isotherm as well as similar pressure-related morphologies as all-atom simulations and experiments. Moreover, the compressibility modulus, order parameter of lipid tails, and thickness of the phospholipid monolayer are quantitatively in line with the all-atom simulations and experiments. This model also captures the sensitive changes in the pressure-area isotherms of mixed DPPC/POPC monolayers with altered mixing ratios, indicating that the model is promising for applications with complex natural phospholipid monolayers. These results demonstrate a significant improvement of quantitative phospholipid monolayer simulations over previous coarse-grained models

Machine learning assisted fast prediction of inertial lift in microchannels

Inertial effect has been extensively used in manipulating both engineered particles and biocolloids in microfluidic platforms. The design of inertial microfluidic devices largely relies on precise prediction of particle migration that is determined by the inertial lift acting on the particle. In spite of being the only means to accurately obtain the lift forces, direct numerical simulation (DNS) often consumes high computational cost and even becomes impractical when applied to microchannels with complex geometries. Herein, we proposed a fast numerical algorithm in conjunction with machine learning techniques for the analysis and design of inertial microfluidic devices. A database of inertial lift forces was first generated by conducting DNS over a wide range of operating parameters in straight microchannels with three types of cross-sectional shapes, including rectangular, triangular and semicircular shapes. A machine learning assisted model was then developed to gain the inertial lift distribution, by simply specifying the cross-sectional shape, Reynolds number and particle blockage ratio. The resultant inertial lift was integrated into the Lagrangian tracking method to quickly predict the particle trajectories in two types of microchannels in practical devices and yield good agreement with experimental observations. Our database and the associated codes allow researchers to expedite the development of the inertial microfluidic devices for particle manipulation

JUNO Sensitivity on Proton Decay p→νˉK+p\to \bar\nu K^+ Searches

The Jiangmen Underground Neutrino Observatory (JUNO) is a large liquid scintillator detector designed to explore many topics in fundamental physics. In this paper, the potential on searching for proton decay in $p\to \bar\nu K^+$ mode with JUNO is investigated.The kaon and its decay particles feature a clear three-fold coincidence signature that results in a high efficiency for identification. Moreover, the excellent energy resolution of JUNO permits to suppress the sizable background caused by other delayed signals. Based on these advantages, the detection efficiency for the proton decay via $p\to \bar\nu K^+$ is 36.9% with a background level of 0.2 events after 10 years of data taking. The estimated sensitivity based on 200 kton-years exposure is $9.6 \times 10^{33}$ years, competitive with the current best limits on the proton lifetime in this channel

JUNO sensitivity on proton decay p → ν K + searches*

The Jiangmen Underground Neutrino Observatory (JUNO) is a large liquid scintillator detector designed to explore many topics in fundamental physics. In this study, the potential of searching for proton decay in the $p\to \bar{\nu} K^+$ mode with JUNO is investigated. The kaon and its decay particles feature a clear three-fold coincidence signature that results in a high efficiency for identification. Moreover, the excellent energy resolution of JUNO permits suppression of the sizable background caused by other delayed signals. Based on these advantages, the detection efficiency for the proton decay via $p\to \bar{\nu} K^+$ is 36.9% ± 4.9% with a background level of $0.2\pm 0.05({\rm syst})\pm$$0.2({\rm stat})$ events after 10 years of data collection. The estimated sensitivity based on 200 kton-years of exposure is $9.6 \times 10^{33}$ years, which is competitive with the current best limits on the proton lifetime in this channel and complements the use of different detection technologies